Even though some \({\mathtt {TFC}}\)  measures [1]}, [2]}, [3]}, [4]}, [5]}, [6]} attempt to take the group effect into consideration, the involved information is still inadequate.
To explain, we find that these measures typically require (i) first representing the affiliation relationship between users and groups as a bipartite graph, where users and groups are two disjoint sets of nodes and affiliation relationships are a set of edges, and (ii) then exploiting information on this bipartite graph. For example, [3]} proposes structural diversity to measure the number of adjacent groups for a given user; [4]}, [5]}, [6]} utilize edge weights between users and groups; [1]}, [2]} measures the commonality of groups between two users on this bipartite graph. However, in these works, contracting a group into a node makes more detailed group information imperceptible, e.g., connectivity.
